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新增: F-05多模态Phase2a后端(ContentPart+parts字段+provider图片块+truncate防撑爆)

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绝尘
2026-06-17 03:09:10 +08:00
parent 71718c1cd8
commit e3cd44802a
5 changed files with 496 additions and 8 deletions
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189
crates/df-ai-core/src/provider.rs
View File

@@ -38,11 +38,69 @@ pub struct CompletionRequest {
pub tool_choice: Option<serde_json::Value>,
}
/// 多模态消息内容片。Text 片为字符串；Image 片可走 url 或 base64（二选一，base64 非空时 url 忽略）。
///
/// F-260614-05 Phase 2a 后端：ContentPart 作为 `ChatMessage.parts` 的元素类型。
/// 设计上 `content: String`（纯文本主载荷）保持不变，多模态片挂在 `parts`：
/// 这样未接入多模态的调用方（audit/title/commands/knowledge_inject 等读 content 当字符串）
/// 零回归，避免一次性改全仓。provider 转换层在 `has_image()` 为真时把 parts 透传给
/// vision 端点，否则只用 content 文本（保持纯文本端点兼容）。
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(tag = "type", rename_all = "snake_case")]
pub enum ContentPart {
/// 文本片
Text { text: String },
/// 图片片。
/// - url：http(s) 可达 URL（provider 直接转发，不读字节）。
/// - base64：data URI 之外的纯 base64 字符串 + media_type（provider 内嵌转发）。
/// base64 非空时 url 忽略，便于前端上行无网络回拉的本地粘贴图。
Image {
url: Option<String>,
base64: Option<String>,
/// base64 模式必填（image/png | image/jpeg | image/webp | image/gif）；url 模式可空。
media_type: Option<String>,
/// 可选 alt（vision 模型/降级文本时用）
#[serde(skip_serializing_if = "Option::is_none")]
alt: Option<String>,
},
}
impl ContentPart {
pub fn text(text: impl Into<String>) -> Self {
ContentPart::Text { text: text.into() }
}
pub fn image_base64(media_type: impl Into<String>, data: impl Into<String>) -> Self {
ContentPart::Image {
url: None,
base64: Some(data.into()),
media_type: Some(media_type.into()),
alt: None,
}
}
pub fn image_url(url: impl Into<String>) -> Self {
ContentPart::Image { url: Some(url.into()), base64: None, media_type: None, alt: None }
}
/// 是否图片片
pub fn is_image(&self) -> bool {
matches!(self, ContentPart::Image { .. })
}
}
/// 聊天消息
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChatMessage {
pub role: MessageRole,
pub content: String,
/// 多模态内容片（F-260614-05 Phase 2a）。
///
/// `None`/空 → 纯文本消息（绝大多数场景，content 即全部载荷）。
/// `Some(含 Image 片)` → 多模态消息，provider 在 `has_image()` 为真时把 parts
/// 连同 content（作为前置 Text 片）一起转成 OpenAI/Anthropic 的 content blocks。
/// content 字段始终保留人类可读文本（文本消息的 Text 片内容与 content 一致），
/// 保证 audit/title/export 等读 content 当字符串的调用方零回归。
#[serde(default, skip_serializing_if = "Option::is_none")]
pub parts: Option<Vec<ContentPart>>,
/// 工具调用 ID（role=Tool 时必填）
#[serde(skip_serializing_if = "Option::is_none")]
pub tool_call_id: Option<String>,
@@ -61,19 +119,50 @@ pub struct ChatMessage {
impl ChatMessage {
pub fn system(content: impl Into<String>) -> Self {
Self { role: MessageRole::System, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
Self { role: MessageRole::System, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None }
}
pub fn user(content: impl Into<String>) -> Self {
Self { role: MessageRole::User, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
Self { role: MessageRole::User, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None }
}
pub fn assistant(content: impl Into<String>) -> Self {
Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: None, model: None, status: None }
Self { role: MessageRole::Assistant, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None }
}
pub fn assistant_with_tools(content: impl Into<String>, tool_calls: Vec<ToolCall>) -> Self {
Self { role: MessageRole::Assistant, content: content.into(), tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None }
Self { role: MessageRole::Assistant, content: content.into(), parts: None, tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None }
}
pub fn tool_result(call_id: impl Into<String>, content: impl Into<String>) -> Self {
Self { role: MessageRole::Tool, content: content.into(), tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None }
Self { role: MessageRole::Tool, content: content.into(), parts: None, tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None }
}
/// 多模态 user 消息：content 文本 + parts（含 Image 片）。
/// content 作为人类可读文本（也作非 vision 端点降级载荷）；parts 透传给 vision 端点。
pub fn user_parts(content: impl Into<String>, parts: Vec<ContentPart>) -> Self {
Self { role: MessageRole::User, content: content.into(), parts: Some(parts), tool_call_id: None, tool_calls: None, model: None, status: None }
}
/// 是否含图片片（供 provider 判定走多模态分支）。
pub fn has_image(&self) -> bool {
self.parts.as_ref().map(|ps| ps.iter().any(|p| p.is_image())).unwrap_or(false)
}
/// parts 若存在则返回引用，否则 None。
pub fn parts(&self) -> Option<&[ContentPart]> {
self.parts.as_deref()
}
/// 把 content + parts 拍平为有序 ContentPart 序列：先 content 作 Text 片（非空时），
/// 再追加 parts（若有）。供 provider 生成 content blocks（保证文本在前、图片在后）。
pub fn flattened_parts(&self) -> Vec<ContentPart> {
let mut out: Vec<ContentPart> = Vec::new();
if !self.content.is_empty() {
out.push(ContentPart::Text { text: self.content.clone() });
}
if let Some(ps) = &self.parts {
for p in ps {
out.push(p.clone());
}
}
out
}
/// 是否处于 active 态（status 为 None 或 "active"）。其余状态一律 false。
@@ -274,4 +363,94 @@ mod tests {
m.status = Some("compressed".to_string());
assert!(!m.is_active(), "compressed 应不 active（白名单隔离）");
}
// ---------- F-260614-05 Phase 2a ContentPart ----------
/// 老 JSON（无 parts 字段）反序列化时 parts 应为 None（向前兼容）
#[test]
fn contentpart_legacy_json_no_parts() {
let json = r#"{"role":"user","content":"hello"}"#;
let m: ChatMessage = serde_json::from_str(json).expect("老 JSON 应可反序列化");
assert_eq!(m.content, "hello");
assert!(m.parts.is_none(), "老 JSON 无 parts 字段 → None");
assert!(!m.has_image());
}
/// 新 JSON 带 parts（含 Image 片）反序列化 round-trip
#[test]
fn contentpart_with_image_roundtrip() {
let m = ChatMessage::user_parts(
"看这张图",
vec![
ContentPart::image_base64("image/png", "iVBORw0KGgo="),
ContentPart::text("说明"),
],
);
let json = serde_json::to_string(&m).expect("序列化");
// parts 顺序：[image_base64, text] → 首元素是 image
assert!(json.contains(r#""parts":[{"type":"image""#));
assert!(json.contains(r#""type":"text""#));
assert!(json.contains(r#""base64":"iVBORw0KGgo=""#));
assert!(json.contains(r#""media_type":"image/png""#));
let back: ChatMessage = serde_json::from_str(&json).expect("反序列化 round-trip");
assert_eq!(back.content, "看这张图");
assert!(back.has_image());
let parts = back.parts.expect("parts 应存在");
assert_eq!(parts.len(), 2);
assert!(parts[0].is_image());
}
/// has_image 判定
#[test]
fn contentpart_has_image_detection() {
assert!(!ChatMessage::user("纯文本").has_image());
let m = ChatMessage::user_parts("t", vec![ContentPart::text("只文本片")]);
assert!(!m.has_image(), "仅 Text 片不算 has_image");
let m = ChatMessage::user_parts(
"t",
vec![ContentPart::text("前缀"), ContentPart::image_url("https://x/a.png")],
);
assert!(m.has_image());
}
/// flattened_parts：content 非空 → 前置 Text 片 + parts 追加
#[test]
fn contentpart_flattened_parts() {
let m = ChatMessage::user("hi");
let flat = m.flattened_parts();
assert_eq!(flat.len(), 1);
assert_eq!(flat[0], ContentPart::Text { text: "hi".into() });
let m = ChatMessage::user_parts(
"cap",
vec![ContentPart::image_url("u"), ContentPart::text("尾")],
);
let flat = m.flattened_parts();
assert_eq!(flat.len(), 3);
assert_eq!(flat[0], ContentPart::Text { text: "cap".into() });
assert!(flat[1].is_image());
assert_eq!(flat[2], ContentPart::Text { text: "尾".into() });
let mut m = ChatMessage::user("");
m.parts = Some(vec![ContentPart::text("x")]);
let flat = m.flattened_parts();
assert_eq!(flat.len(), 1, "空 content 不应产生空 Text 片");
}
/// 向后兼容：旧代码 ChatMessage 字面量构造仍合法（audit/title/commands 零回归）
#[test]
fn contentpart_struct_literal_compat() {
let m = ChatMessage {
role: MessageRole::User,
content: "字面量构造".into(),
parts: None,
tool_call_id: None,
tool_calls: None,
model: None,
status: None,
};
assert_eq!(m.content, "字面量构造");
assert!(m.parts.is_none());
}
}

104
crates/df-ai/src/anthropic_compat.rs
View File

@@ -18,6 +18,9 @@ use crate::provider::{
CompletionRequest, CompletionResponse, LlmProvider, MessageRole,
StreamChunk, StreamResult, TokenUsage, ToolCall, ToolCallDelta,
};
// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
#[cfg(test)]
use crate::provider::ChatMessage;
use crate::retry::{
retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
};
@@ -330,8 +333,50 @@ impl AnthropicCompatProvider {
}
MessageRole::User => {
Self::flush_tool_results(&mut messages, &mut pending_tool_results);
// F-260614-05 Phase 2a: 多模态 user 消息 → content blocks 数组（text/image）。
// 含图时把 content + parts 拍平成 blocks：Text 片 → {type:text}，
// Image 片 → {type:image, source:{type:base64, media_type, data}}。
// Anthropic 协议要求 image 必须内嵌 base64（不接受 URL 直传），
// url 模式应由 commands 层预拉字节回填 base64（provider 不发额外 HTTP）。
// 纯文本消息（无图）保持原字符串简写，与现有端点零回归。
if m.has_image() {
let blocks: Vec<serde_json::Value> = m
.flattened_parts()
.into_iter()
.map(|p| match p {
crate::provider::ContentPart::Text { text } => serde_json::json!({
"type": "text",
"text": text,
}),
crate::provider::ContentPart::Image { url, base64, media_type, alt: _ } => {
let mt = media_type.clone().unwrap_or_else(|| "image/png".into());
let data = base64.clone().unwrap_or_else(|| {
// url 模式无 base64 时 provider 层兜底（commands 层应已预拉）：
// 发空 data 会让 Anthropic 报错，warn 提示但不阻塞。
if url.is_some() {
warn!(
url = ?url,
"Anthropic user 消息含 Image(url) 但未预拉 base64，将发空 data（commands 层应回填 base64）"
);
}
String::new()
});
serde_json::json!({
"type": "image",
"source": {
"type": "base64",
"media_type": mt,
"data": data,
}
})
}
})
.collect();
messages.push(serde_json::json!({ "role": "user", "content": blocks }));
} else {
messages.push(serde_json::json!({ "role": "user", "content": m.content }));
}
}
MessageRole::Assistant => {
Self::flush_tool_results(&mut messages, &mut pending_tool_results);
let mut content: Vec<serde_json::Value> = Vec::new();
@@ -742,4 +787,63 @@ mod tests {
assert_eq!(c.delta, "");
assert!(acc.is_none());
}
// ---------- F-260614-05 Phase 2a 多模态 convert_request ----------
/// 含图 user 消息 → content blocks（text + image source.base64）
#[test]
fn anthropic_convert_multimodal_user_blocks() {
let provider = AnthropicCompatProvider::new("https://api.anthropic.com", "k", "claude-3-5-sonnet");
let req = CompletionRequest {
model: "claude-3-5-sonnet".into(),
messages: vec![ChatMessage::user_parts(
"看图",
vec![crate::provider::ContentPart::image_base64("image/png", "iVBOR")],
)],
temperature: None,
max_tokens: None,
stream: false,
tools: None,
tool_choice: None,
};
let body = provider.convert_request(req);
// user 消息 content 应为数组形态
let user_msg = body
.messages
.iter()
.find(|m| m.get("role").and_then(|r| r.as_str()) == Some("user"))
.expect("应有 user 消息");
let content = user_msg.get("content").and_then(|c| c.as_array()).expect("user content 应为数组");
// 顺序：content "看图" → text 块，image 片 → image 块
assert_eq!(content.len(), 2);
assert_eq!(content[0]["type"], "text");
assert_eq!(content[0]["text"], "看图");
assert_eq!(content[1]["type"], "image");
assert_eq!(content[1]["source"]["type"], "base64");
assert_eq!(content[1]["source"]["media_type"], "image/png");
assert_eq!(content[1]["source"]["data"], "iVBOR");
}
/// 纯文本 user 消息 → content 仍是字符串简写（无图不数组化）
#[test]
fn anthropic_convert_text_only_user_remains_string() {
let provider = AnthropicCompatProvider::new("https://api.anthropic.com", "k", "claude-3-5-sonnet");
let req = CompletionRequest {
model: "claude-3-5-sonnet".into(),
messages: vec![ChatMessage::user("hello")],
temperature: None,
max_tokens: None,
stream: false,
tools: None,
tool_choice: None,
};
let body = provider.convert_request(req);
let user_msg = body
.messages
.iter()
.find(|m| m.get("role").and_then(|r| r.as_str()) == Some("user"))
.expect("应有 user 消息");
// 纯文本 → 字符串简写（非数组）
assert_eq!(user_msg.get("content").and_then(|c| c.as_str()), Some("hello"));
}
}

96
crates/df-ai/src/openai_compat.rs
View File

@@ -16,6 +16,9 @@ use crate::provider::{
CompletionRequest, CompletionResponse, LlmProvider, StreamChunk, StreamResult,
TokenUsage, ToolCall, ToolCallDelta,
};
// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
#[cfg(test)]
use crate::provider::ChatMessage;
use crate::retry::{
retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
};
@@ -44,10 +47,13 @@ struct OpenAiRequest {
}
/// OpenAI 消息格式
///
/// `content` 为 `serde_json::Value`：纯文本消息走字符串简写（与老端点兼容），
/// 含图消息走 `[{type:"text",text},{type:"image_url",image_url:{url}}]` 数组。
#[derive(Debug, Clone, Serialize, Deserialize)]
struct OpenAiMessage {
role: String,
content: String,
content: serde_json::Value,
#[serde(skip_serializing_if = "Option::is_none")]
tool_call_id: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
@@ -308,6 +314,38 @@ impl OpenAICompatProvider {
crate::provider::MessageRole::Assistant => "assistant",
crate::provider::MessageRole::Tool => "tool",
};
// F-260614-05 Phase 2a: 多模态 content（须在 move m.tool_calls 之前算，借用 m）。
// 含图消息走 content 数组（text/image_url）；纯文本走字符串简写
// （保持与现有纯文本端点零回归）。image_url 支持 data URI（base64）与 http(s) URL。
let content = if m.has_image() {
let parts: Vec<serde_json::Value> = m
.flattened_parts()
.into_iter()
.map(|p| match p {
crate::provider::ContentPart::Text { text } => serde_json::json!({
"type": "text",
"text": text,
}),
crate::provider::ContentPart::Image { url, base64, media_type, alt: _ } => {
let final_url = match (base64, url, media_type) {
(Some(b), _, Some(mt)) => {
format!("data:{};base64,{}", mt, b)
}
(None, Some(u), _) => u,
// 兜底：缺数据时退化为占位，避免发空 url 触发 400
_ => String::new(),
};
serde_json::json!({
"type": "image_url",
"image_url": { "url": final_url },
})
}
})
.collect();
serde_json::Value::Array(parts)
} else {
serde_json::Value::String(m.content.clone())
};
let tool_calls = m.tool_calls.map(|calls| {
calls
.into_iter()
@@ -325,7 +363,7 @@ impl OpenAICompatProvider {
});
OpenAiMessage {
role: role.to_string(),
content: m.content,
content,
tool_call_id: m.tool_call_id,
tool_calls,
}
@@ -691,4 +729,58 @@ mod tests {
assert_eq!(tcs[0].function_arguments.as_deref(), Some("{\"q\":"));
assert!(!c.finished);
}
// ---------- F-260614-05 Phase 2a 多模态 convert_request ----------
/// 含图消息 → content 数组（text + image_url data URI）；纯文本 → 字符串简写
#[test]
fn openai_convert_multimodal_content() {
let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
let req = CompletionRequest {
model: "gpt-4o".into(),
messages: vec![ChatMessage::user_parts(
"看图",
vec![
crate::provider::ContentPart::image_base64("image/png", "iVBOR"),
crate::provider::ContentPart::image_url("https://x/a.png"),
],
)],
temperature: None,
max_tokens: None,
stream: false,
tools: None,
tool_choice: None,
};
let out = provider.convert_request(req);
let msg = &out.messages[0];
// content 是数组：[text "看图", image_url(data URI), image_url(http url)]
let arr = msg.content.as_array().expect("含图 → content 数组");
assert_eq!(arr.len(), 3);
assert_eq!(arr[0]["type"], "text");
assert_eq!(arr[0]["text"], "看图");
assert_eq!(arr[1]["type"], "image_url");
assert_eq!(
arr[1]["image_url"]["url"],
"data:image/png;base64,iVBOR"
);
assert_eq!(arr[2]["image_url"]["url"], "https://x/a.png");
}
/// 纯文本消息 → content 仍是字符串简写（无图不数组化，对齐纯文本端点兼容）
#[test]
fn openai_convert_text_only_remains_string() {
let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
let req = CompletionRequest {
model: "gpt-4o".into(),
messages: vec![ChatMessage::user("hello")],
temperature: None,
max_tokens: None,
stream: false,
tools: None,
tool_choice: None,
};
let out = provider.convert_request(req);
let msg = &out.messages[0];
assert_eq!(msg.content, serde_json::Value::String("hello".into()));
}
}

112
src-tauri/src/commands/ai/conversation.rs
View File

@@ -74,6 +74,58 @@ pub(crate) fn truncate_for_persist(content: &str) -> String {
)
}
/// 落库前对 `ChatMessage.parts` 做截断(F-260614-05 Phase 2a)。
///
/// - `Text` 片:复用 `truncate_for_persist` 头尾截断语义(单 Text 片 > 50KB 才截)。
/// - `Image` 片:base64 通常已 50KB+,落库前替换为占位 `Text` 片
/// `<image: base64 已省略, 共 N 字节>`,避免大体量图把对话 JSON 撑爆
/// (一张 1MB PNG 的 base64 ≈ 1.3MB 字符串)。原 Image 片仅在内存真相源(ContextManager)
/// 保留,重发时仍带图——对齐现有「持久化视图不污染内存真相源」约定。
/// - `Image` url 模式(无 base64):url 本身短,原样保留。
///
/// 返回 None 表示 parts 无需保留(全空或全部被替换且原 parts 仅含图);调用方据此清空 parts。
pub(crate) fn truncate_parts_for_persist(parts: &[df_ai::provider::ContentPart]) -> Option<Vec<df_ai::provider::ContentPart>> {
use df_ai::provider::ContentPart;
let mut out: Vec<ContentPart> = Vec::with_capacity(parts.len());
let mut changed = false;
for p in parts {
match p {
ContentPart::Text { text } => {
let truncated = truncate_for_persist(text);
if truncated.len() != text.len() {
changed = true;
}
out.push(ContentPart::Text { text: truncated });
}
ContentPart::Image { url, base64, media_type, alt } => {
// base64 模式:体量大,替换占位 Text 片
if let Some(b) = base64 {
let bytes_approx = b.len(); // base64 字符数 ≈ 字节数 * 4/3,粗估
let placeholder = format!("<image: base64 已省略, 共约 {} 字符>", bytes_approx);
out.push(ContentPart::Text { text: placeholder });
changed = true;
} else {
// url 模式:url 短,原样保留(含 media_type/alt)
out.push(ContentPart::Image {
url: url.clone(),
base64: None,
media_type: media_type.clone(),
alt: alt.clone(),
});
}
}
}
}
if out.is_empty() {
None
} else if changed {
Some(out)
} else {
// 无变化:返回克隆的原 parts(保持引用语义一致)
Some(parts.to_vec())
}
}
/// 保存对话到数据库（按 conv_id 写库，不受 active_conversation_id 切换影响）
///
/// 写 messages + updated_at + 累加 token 用量 + 首次落库的 model；标题由 ensure_conversation_title 单独生成。
@@ -95,6 +147,11 @@ pub(crate) async fn save_conversation(
let mut msgs = session.messages.all_messages_clone();
for m in &mut msgs {
m.content = truncate_for_persist(&m.content);
// F-260614-05 Phase 2a: parts(Image base64) 同样截断(替换占位 Text 片),
// 防大体量图把对话 JSON 撑爆。仅作用于持久化副本,不污染内存真相源。
if let Some(parts) = m.parts.as_ref() {
m.parts = truncate_parts_for_persist(parts);
}
}
(
serde_json::to_string(&msgs).unwrap_or_else(|_| "[]".to_string()),
@@ -269,4 +326,59 @@ mod tests {
assert!(result.ends_with('中'));
assert!(result.contains("已截断"));
}
// ---------- F-260614-05 Phase 2a truncate_parts_for_persist ----------
#[test]
fn truncate_parts_image_base64_replaced_with_placeholder() {
use df_ai::provider::ContentPart;
let parts = vec![
ContentPart::text("前缀文本"),
ContentPart::image_base64("image/png", &"A".repeat(60_000)),
ContentPart::text("后缀"),
];
let out = truncate_parts_for_persist(&parts).expect("应有结果");
// Image base64 被替换为占位 Text 片
assert!(out.iter().all(|p| !matches!(p, ContentPart::Image { base64: Some(_), .. })));
let placeholder = out.iter().find_map(|p| match p {
ContentPart::Text { text } if text.contains("base64 已省略") => Some(text.clone()),
_ => None,
}).expect("应含占位 Text 片");
assert!(placeholder.contains("60000"));
// 非 base64 的 Text 片保留原值
assert!(out.iter().any(|p| matches!(p, ContentPart::Text { text } if text == "前缀文本")));
assert!(out.iter().any(|p| matches!(p, ContentPart::Text { text } if text == "后缀")));
}
#[test]
fn truncate_parts_image_url_preserved() {
use df_ai::provider::ContentPart;
// url 模式:url 短,原样保留(含 media_type)
let parts = vec![ContentPart::Image {
url: Some("https://x/a.png".into()),
base64: None,
media_type: None,
alt: None,
}];
let out = truncate_parts_for_persist(&parts).expect("应有结果");
assert!(matches!(out[0], ContentPart::Image { ref url, .. } if url.as_deref() == Some("https://x/a.png")));
}
#[test]
fn truncate_parts_long_text_part_truncated() {
use df_ai::provider::ContentPart;
// 单 Text 片超阈值 → 头尾截断
let long: String = "x".repeat(TRUNCATE_THRESHOLD + 1000);
let parts = vec![ContentPart::Text { text: long }];
let out = truncate_parts_for_persist(&parts).expect("应有结果");
match &out[0] {
ContentPart::Text { text } => assert!(text.contains("已截断"), "超长 Text 片应被截断"),
_ => panic!("应仍是 Text 片"),
}
}
#[test]
fn truncate_parts_empty_returns_none() {
assert_eq!(truncate_parts_for_persist(&[]), None);
}
}

1
src-tauri/src/commands/ai/title.rs
View File

@@ -50,6 +50,7 @@ pub(crate) async fn ensure_conversation_title(
.map(|m| ChatMessage {
role: m.role.clone(),
content: m.content.clone(),
parts: None,
tool_call_id: None,
tool_calls: None,
model: None,